Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data
Styliani I. Kampezidou,
Archana Tikayat Ray,
Anirudh Prabhakara Bhat
et al.
Abstract:This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (… Show more
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